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Oracle NoSQL DB & InfiniteGraph - Trends in Big Data and Graph Technology


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Join Oracle NoSQL DB and InfiniteGraph development teams in a discussion of the latest trends in Big Data and Graph Technology. Learn what Oracle’s view of Big Data is and how Oracle NoSQL Database technologies enable you to manage vast amounts of real-time key-value data.

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Oracle NoSQL DB & InfiniteGraph - Trends in Big Data and Graph Technology

  1. 1. Latest Trends in Big Data and Graph Database technologies Brian Clark, VP Product Management on August 16th, 2012
  2. 2. Overview• The Big Data Problem• Current Big Data Analytics• NoSQL Technologies• Relationship Analytics• InfiniteGraph and NoSQL DB
  3. 3. The Big Data Problem
  4. 4. The Big Data ProblemInformation Overload!Making sense of it all takes time and $$$•Volume - vast amount of data•Velocity - rate of input, rate of change•Variety – structured, un-structured, semi-structured•Value –analytics to gain understanding from the data and relationships•Veracity – truth or meaning of the data and relationships
  5. 5. A Typical “Big Data” Analytics Setup Data Aggregation and Analytics Applications Commodity Linux Platforms and/or High Performance Computing Clusters Column Data Graph Object K-V RDBMS Hadoop Doc DB Store W/H DB DB Store Structured Semi-Structured Unstructured
  6. 6. Incremental Improvements Aren’t EnoughAll current solutions use the same basic architectural model• None of the current solutions have a way to store connections between entities in different silos• Most analytic technology focuses on the content of the data nodes, rather than the many kinds of connections between the nodes and the data in those connections• Why? Because relational and most NoSQL solutions are bad at handling relationships.• Object and Graph databases can efficiently store, manage and query the many kinds of relationships hidden in the data.
  7. 7. NoSQL Technologies
  8. 8. Not Only SQL – a group of 4 primary technologies• Users choose between four different primary technologies for different purposes: – Key-Value Stores – “Big Table” Clones – Document Databases – Object and Graph databases (including InfiniteGraph)• Many implementations sacrifice consistency (ACID transactions, CAP – eventual consistency) for performance.• Technologies such as Objectivity/DB and InfiniteGraph offer ACID transactions, with consistency and performance.
  9. 9. The NoSQL Market
  10. 10. Relationship Analytics
  11. 11. Example 1 - Market AnalysisThe 10 companies that control a majority of U.S. consumer goods brands
  12. 12. Example 2 - DemographicsUsed in social network analysis, marketing, medical research etc.
  13. 13. Example 3 - Seed To Consumer Tracking ?
  14. 14. Example 4 - Ad Placement NetworksSmartphone Ad placement - based on the the user’s profile and location data captured by opt-in applications.• The location data can be stored and distilled in a key-value and column store hybrid database, such as Cassandra• The locations are matched with geospatial data to deduce user interests.• As Ad placement orders arrive, an application built on a graph database such as InfiniteGraph, matches groups of users with Ads:• Maximizes relevance for the user.• Yields maximum value for the advertiser and the placer.
  15. 15. Example 5 - Healthcare InformaticsProblem: Physicians need better electronic records for managing patient data on a global basis and match symptoms, causes, treatments and interdependencies to improve diagnoses and outcomes.• Solution: Create a database capable of leveraging existing architecture using NOSQL tools such as Objectivity/DB and InfiniteGraph that can handle data capture, symptoms, diagnoses, treatments, reactions to medications, interactions and progress.• Result: It works: • Diagnosis is faster and more accurate • The knowledge base tracks similar medical cases. • Treatment success rates have improved.
  16. 16. The Polyglot Approach
  18. 18. ...SUMMARY: A Polyglot Approach Works Best
  19. 19. InfiniteGraphThe Big Data Connection Platform